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Statistical information and signal processing for the underwater internet of things

Abstract : There has been recently a large development of human activities associated to the ocean world, where no standard has emerged for the Internet of Things (IoT) linked to marine autonomous objects. Though it has a limited bandwidth, the acoustic wave is the only way to communicate over average to large distances and it is thus used by many underwater systems to communicate, navigate, or infer information about the environment. This led to a high demand for wireless networks that require both spectral efficiency and energy efficiency with the associated low-complexity algorithms. Therefore, in this Ph.D. thesis, we proposed several original solutions to face this challenge.Indeed, due to the inherent Signal Space Diversity (SSD), rotated constellations allow better theoretical performance than conventional constellations with no spectral spoilage. We review the structural properties of uniformly projected rotated M-QAM constellations, so as to propose a low complexity soft demapping technique for fading channels. Then, we present an original blind technique for the reduction of the PAPR for OFDM systems using the rotated constellations with SSD. In order to reduce the complexity of blind decoding for this technique, we again rely on the properties of uniformly projected M-QAM rotated constellations to design a low-complexity estimator. Moreover, to face the selectivity of the acoustic channel, we suggest a sparse adaptive turbo detector with only a few taps to be updated in order to lower down the complexity burden. Finally, we have proposed an original self-optimized algorithm for which the step-sizes of both the equalizer and the phase estimator are updated adaptively and assisted by soft-information in an iterative manner, so as to meet the requirement of fast convergence and low MSE over time-varying channels.
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https://tel.archives-ouvertes.fr/tel-03179373
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Submitted on : Wednesday, March 24, 2021 - 11:46:09 AM
Last modification on : Wednesday, May 11, 2022 - 3:20:03 PM

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  • HAL Id : tel-03179373, version 1

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Zi Ye. Statistical information and signal processing for the underwater internet of things. Networking and Internet Architecture [cs.NI]. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAE004⟩. ⟨tel-03179373⟩

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